[In this] system authors compared results of low alert states to known influenza epidemics and to data containing emergency room visits, pharmacy purchases and absenteeism. Although the peak incidence of the simulated outbreak is larger than the peak incidence seen in the population data, the simulation results are temporally similar to those seen in the population data. They hoped that this simulation framework will allow them to ask ‘what-if’ questions regarding appropriate response and detection strategies for both natural and man-made epidemics. This is a city scale multi-agent model of weaponized bioterrorist attacks for intelligence and training. At present the model is running with 100,000 agents (this number will be increased). All agents have real social networks and the model contains real city data -hospitals, schools etc. Agents are as realistic as possible and contain a cognitive model.

DYNET—Dynamic Networks. The team is building a model of how networks adapt, evolve and change in response to various types of attacks e.g. infowar or assassination.

[page 105]

We have compared results of low alert states to known influenza epidemics and to data containing emergency room visits, pharmacy purchases and absenteeism. Although the peak incidence of the simulated outbreak is larger than the peak incidence seen in the population data, the simulation results are temporally similar to those seen in the population data. [...]. It is hoped that this simulation framework will allow us to ask ‘what-if’ questions regarding appropriate response and detection strategies for both natural and man-made epidemics.

[page 18]

this is a cityscale multi-agent model of weaponized bioterrorist attacks for intelligence and training. At present the model is running with 100,000 agents (this number will be increased). All agents have real social networks and the model contains real city data - hospitals, schools etc. Agents are as realistic as possible and contain a cognitive model.

[...]

DYNET – Dynamic Networks. The team is building a model of how networks adapt, evolve and change in response to various types of attacks e.g. infowar or assassination

NETEST – It is based on the combination of multi-agent technology with hierarchical Bayesian inference models and biased net models to produce accurate posterior network representations. Bayesian inference models produce representations of a network’s structure and informant accuracy by combining prior network and accuracy data with informant perceptions of a network. Biased net theory examines and captures the biases that may be present within a specific network or group of networks. NETEST provides functionalities to estimate a network’s size, determine its membership and structure, determine areas of the network where data is missing, perform cost and benefit analysis of additional information, assess group level capabilities [embedded in the network, and pose “what if” scenarios to destabilize a network and predict its evolution over time.]

NETEST is a tool that combines multiagent technology with hierarchical Bayesian inference models and biased net models to produce accurate posterior representations of a network. Bayesian inference models produce representations of a network’s structure and informant accuracy by combining prior network and accuracy data with informant perceptions of a network. Biased net theory examines and captures the biases that may exist in a specific network or set of networks. Using NETEST, an investigator has the power to estimate a network’s size, determine its membership and structure, determine areas of the network where data is missing, perform cost/benefit analysis of additional information, assess group level capabilities embedded in the network, and pose “what if” scenarios to destabilize a network and predict its evolution over time.

Anmerkungen

The source is given in the bibliography, but nowhere close to this text fragment.